{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-06T16:20:10.783884Z",
     "start_time": "2020-05-06T16:20:08.102891Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/eason/anaconda3/envs/untitled/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/eason/anaconda3/envs/untitled/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/eason/anaconda3/envs/untitled/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/eason/anaconda3/envs/untitled/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/eason/anaconda3/envs/untitled/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/eason/anaconda3/envs/untitled/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "import defense\n",
    "import os\n",
    "import inceptionv3\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import utils\n",
    "import albumentations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-06T16:20:11.000535Z",
     "start_time": "2020-05-06T16:20:10.786067Z"
    }
   },
   "outputs": [],
   "source": [
    "config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))\n",
    "sess = tf.Session(config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-06T16:20:11.102905Z",
     "start_time": "2020-05-06T16:20:11.004054Z"
    }
   },
   "outputs": [],
   "source": [
    "cleandata = np.load(\"./data/clean100data.npy\")\n",
    "cleanlabel = np.load(\"./data/clean100label.npy\")\n",
    "targets = np.load(\"./data/random_targets.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-06T16:20:11.125436Z",
     "start_time": "2020-05-06T16:20:11.112650Z"
    }
   },
   "outputs": [],
   "source": [
    "l2_x = tf.placeholder(tf.float32, (None,299, 299, 3))\n",
    "l2_orig = tf.placeholder(tf.float32, (None,299, 299, 3))\n",
    "normalized_l2_loss = tf.nn.l2_loss(l2_orig - l2_x) / tf.nn.l2_loss(l2_orig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-06T16:20:16.071648Z",
     "start_time": "2020-05-06T16:20:11.127413Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Scale of 0 disables regularizer.\n",
      "WARNING:tensorflow:From /home/eason/anaconda3/envs/untitled/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From /home/eason/anaconda3/envs/untitled/lib/python3.7/site-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use standard file APIs to check for files with this prefix.\n",
      "INFO:tensorflow:Restoring parameters from /home/eason/Research/Paris/ALL_Defense/data/inception_v3.ckpt\n"
     ]
    }
   ],
   "source": [
    "x = tf.placeholder(tf.float32, (None, 299, 299, 3))\n",
    "\n",
    "session_logits, session_preds = inceptionv3.model(sess, x)\n",
    "session_l2_x = tf.placeholder(tf.float32, (299, 299, 3))\n",
    "session_l2_orig = tf.placeholder(tf.float32, (299, 299, 3))\n",
    "session_normalized_l2_loss = tf.nn.l2_loss(session_l2_orig - session_l2_x) / tf.nn.l2_loss(session_l2_orig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def defended(imgbatch,tran=0.16,scal=0.16,rota=4):\n",
    "    defbatch = np.zeros_like(imgbatch)\n",
    "    for i in range(ae_data.shape[0]):\n",
    "        defbatch[i]=augimg(ae_data[i],tran=tran,scal=scal,rota=rota)\n",
    "    return defbatch\n",
    "\n",
    "def augimg(img,tran=0.16,scal=0.16,rota=4):\n",
    "    aug = albumentations.ShiftScaleRotate(shift_limit=tran,scale_limit=scal,rotate_limit=rota,p=1,border_mode=0)\n",
    "    augmented = aug(image=img.astype(np.float32))\n",
    "    auged = augmented['image']\n",
    "    return auged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def defend_test(img):\n",
    "    aug = albumentations.ImageCompression(quality_lower=20,quality_upper=80,compression_type=1,p=1)\n",
    "    augmented = aug(image=(img*255).astype(np.uint8))\n",
    "    auged = augmented['image']/255\n",
    "    return auged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def defended(imgbatch):\n",
    "    defbatch = np.zeros_like(imgbatch)\n",
    "    for i in range(ae_data.shape[0]):\n",
    "        defbatch[i]=defense.defend_CROP(ae_data[i])\n",
    "    return defbatch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./AE/PGD_AE_data.npy\n",
      "Accuracy: 0.21\n",
      "Attack success rate: 0.33\n"
     ]
    }
   ],
   "source": [
    "totalbatch = 10\n",
    "batchsize = 10\n",
    "ae_dir = \"./AE\"\n",
    "names = os.listdir(ae_dir)\n",
    "for name in names:\n",
    "    path = os.path.join(ae_dir, name)\n",
    "    if os.path.splitext(path)[-1] == '.npy':\n",
    "        print(path)\n",
    "        ae_data = np.load(path)\n",
    "#         ae_FD = defended(ae_data)\n",
    "        ae_FD = np.copy(ae_data)\n",
    "        preds = np.zeros((100))\n",
    "        for numbatch in range(totalbatch):\n",
    "            aebatch, _ = utils.getabatch(ae_FD, cleanlabel, numbatch, batchsize)\n",
    "            preds[numbatch*batchsize:(numbatch*batchsize+batchsize)] = sess.run([session_preds], {x: aebatch})[0]\n",
    "        print(\"Accuracy:\", np.mean(preds == cleanlabel))\n",
    "        print(\"Attack success rate:\", np.mean(preds == targets))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa0707cd810>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(ae_data[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# No defense Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-05T14:18:10.824154Z",
     "start_time": "2020-05-05T14:18:04.764563Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./AE/PGD_AE_Acc.npy\n",
      "L2: [0.00027422523]\n",
      "Accuracy: 0.0\n",
      "Attack success rate: 1.0\n"
     ]
    }
   ],
   "source": [
    "ae_dir = \"./AE\"\n",
    "names = os.listdir(ae_dir)\n",
    "for name in names:\n",
    "    path = os.path.join(ae_dir, name)\n",
    "    if os.path.splitext(path)[-1] == '.npy' and name.find('AE') != -1:\n",
    "        print(path)\n",
    "        ae_data = np.load(path)\n",
    "        preds = np.zeros((100))\n",
    "#         print(\"Linf\", linf_distortion(ae_data, cleandata))\n",
    "        l2 = sess.run([normalized_l2_loss], {l2_x: ae_data, l2_orig: cleandata})\n",
    "        print(\"L2:\", l2)\n",
    "        for numbatch in range(totalbatch):\n",
    "            aebatch, _ = utils.getabatch(ae_data, cleanlabel, numbatch, batchsize)\n",
    "            preds[numbatch*batchsize:(numbatch*batchsize+batchsize)] = sess.run([session_preds], {x: aebatch})[0]\n",
    "        print(\"Accuracy:\", np.mean(preds == cleanlabel))\n",
    "        print(\"Attack success rate:\", np.mean(preds == targets))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./AE/LBFGS_AE_data.npy\n",
      "Accuracy: 0.92\n",
      "Attack success rate: 0.0\n",
      "./AE/clean100data.npy\n",
      "Accuracy: 0.95\n",
      "Attack success rate: 0.0\n",
      "./AE/CW_AE_data.npy\n",
      "Accuracy: 0.92\n",
      "Attack success rate: 0.0\n",
      "./AE/IFGSM_003_AE_data.npy\n",
      "Accuracy: 0.9\n",
      "Attack success rate: 0.0\n",
      "./AE/FGSM_003_AE_data.npy\n",
      "Accuracy: 0.93\n",
      "Attack success rate: 0.0\n",
      "./AE/IFGSM_001_AE_data.npy\n",
      "Accuracy: 0.97\n",
      "Attack success rate: 0.0\n",
      "./AE/FGSM_001_AE_data.npy\n",
      "Accuracy: 0.94\n",
      "Attack success rate: 0.0\n"
     ]
    }
   ],
   "source": [
    "ae_dir = \"./AE\"\n",
    "names = os.listdir(ae_dir)\n",
    "for name in names:\n",
    "    path = os.path.join(ae_dir, name)\n",
    "    if os.path.splitext(path)[-1] == '.npy':\n",
    "        print(path)\n",
    "        ae_data = np.load(path)\n",
    "        ae_FD = defended(ae_data)\n",
    "        preds = np.zeros((100))\n",
    "        for numbatch in range(totalbatch):\n",
    "            aebatch, _ = getabatch(ae_FD, cleanlabel, numbatch, batchsize)\n",
    "            preds[numbatch*batchsize:(numbatch*batchsize+batchsize)] = sess.run([session_preds], {x: aebatch})[0]\n",
    "        print(\"Accuracy:\", np.mean(preds == cleanlabel))\n",
    "        print(\"Attack success rate:\", np.mean(preds == targets))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FD Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-05T14:33:21.568807Z",
     "start_time": "2020-05-05T14:31:01.000382Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./AE/LBFGS_AE_data.npy\n",
      "Accuracy: 0.93\n",
      "Attack success rate: 0.0\n",
      "./AE/clean100data.npy\n",
      "Accuracy: 0.93\n",
      "Attack success rate: 0.0\n",
      "./AE/CW_AE_data.npy\n",
      "Accuracy: 0.81\n",
      "Attack success rate: 0.02\n",
      "./AE/IFGSM_003_AE_data.npy\n",
      "Accuracy: 0.87\n",
      "Attack success rate: 0.01\n",
      "./AE/FGSM_003_AE_data.npy\n",
      "Accuracy: 0.49\n",
      "Attack success rate: 0.0\n",
      "./AE/IFGSM_001_AE_data.npy\n",
      "Accuracy: 0.91\n",
      "Attack success rate: 0.0\n",
      "./AE/FGSM_001_AE_data.npy\n"
     ]
    }
   ],
   "source": [
    "ae_dir = \"./AE\"\n",
    "names = os.listdir(ae_dir)\n",
    "for name in names:\n",
    "    path = os.path.join(ae_dir, name)\n",
    "    if os.path.splitext(path)[-1] == '.npy':\n",
    "        print(path)\n",
    "        ae_data = np.load(path)\n",
    "        ae_FD = defense.defended('SHIELD',ae_data)\n",
    "        preds = np.zeros((100))\n",
    "        for numbatch in range(totalbatch):\n",
    "            aebatch, _ = getabatch(ae_FD, cleanlabel, numbatch, batchsize)\n",
    "            preds[numbatch*batchsize:(numbatch*batchsize+batchsize)] = sess.run([session_preds], {x: aebatch})[0]\n",
    "        print(\"Accuracy:\", np.mean(preds == cleanlabel))\n",
    "        print(\"Attack success rate:\", np.mean(preds == targets))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GD Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-05T16:07:18.763099Z",
     "start_time": "2020-05-05T16:07:13.276617Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./AE/FGSM_003_AE_data.npy\n",
      "Accuracy: 0.56\n",
      "Attack success rate: 0.0\n",
      "./AE/CW_AE_data.npy\n",
      "Accuracy: 0.83\n",
      "Attack success rate: 0.0\n",
      "./AE/IFGSM_001_AE_data.npy\n",
      "Accuracy: 0.92\n",
      "Attack success rate: 0.0\n",
      "./AE/FGSM_001_AE_data.npy\n",
      "Accuracy: 0.66\n",
      "Attack success rate: 0.0\n",
      "./AE/clean100data.npy\n",
      "Accuracy: 0.96\n",
      "Attack success rate: 0.0\n",
      "./AE/IFGSM_003_AE_data.npy\n",
      "Accuracy: 0.75\n",
      "Attack success rate: 0.04\n",
      "./AE/Deepfool_AE_data.npy\n",
      "Accuracy: 0.98\n",
      "Attack success rate: 0.0\n"
     ]
    }
   ],
   "source": [
    "ds = 0.15\n",
    "ae_dir = \"./AE\"\n",
    "names = os.listdir(ae_dir)\n",
    "for name in names:\n",
    "    path = os.path.join(ae_dir, name)\n",
    "    if os.path.splitext(path)[-1] == '.npy':\n",
    "        print(path)\n",
    "        ae_data = np.load(path)\n",
    "        ae_GD = np.zeros(ae_data.shape)\n",
    "        for i in range(ae_data.shape[0]):\n",
    "            ae_GD[i] = defend_GD(ae_data[i], distort_limit=ds)\n",
    "        preds = np.zeros((100))\n",
    "        for numbatch in range(totalbatch):\n",
    "            aebatch, _ = getabatch(ae_GD, cleanlabel, numbatch, batchsize)\n",
    "            preds[numbatch*batchsize:(numbatch*batchsize+batchsize)] = sess.run([session_preds], {x: aebatch})[0]\n",
    "        print(\"Accuracy:\", np.mean(preds == cleanlabel))\n",
    "        print(\"Attack success rate:\", np.mean(preds == np.argmax(targets, axis=1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FD+GD Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-05T15:04:57.111883Z",
     "start_time": "2020-05-05T15:02:29.124716Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./AE/FGSM_003_AE_data.npy\n",
      "Accuracy: 0.55\n",
      "Attack success rate: 0.0\n",
      "./AE/CW_AE_data.npy\n",
      "Accuracy: 0.83\n",
      "Attack success rate: 0.0\n",
      "./AE/IFGSM_001_AE_data.npy\n",
      "Accuracy: 0.91\n",
      "Attack success rate: 0.0\n",
      "./AE/FGSM_001_AE_data.npy\n",
      "Accuracy: 0.75\n",
      "Attack success rate: 0.0\n",
      "./AE/clean100data.npy\n",
      "Accuracy: 0.93\n",
      "Attack success rate: 0.0\n",
      "./AE/IFGSM_003_AE_data.npy\n",
      "Accuracy: 0.89\n",
      "Attack success rate: 0.0\n",
      "./AE/Deepfool_AE_data.npy\n",
      "Accuracy: 0.94\n",
      "Attack success rate: 0.0\n"
     ]
    }
   ],
   "source": [
    "ds = 0.25\n",
    "ae_dir = \"./AE\"\n",
    "names = os.listdir(ae_dir)\n",
    "for name in names:\n",
    "    path = os.path.join(ae_dir, name)\n",
    "    if os.path.splitext(path)[-1] == '.npy':\n",
    "        print(path)\n",
    "        ae_data = np.load(path)\n",
    "        ae_FD_GD = np.zeros(ae_data.shape)\n",
    "        for i in range(ae_data.shape[0]):\n",
    "            ae_FD_GD[i] = defend_GD(defend_FD_sig(ae_data[i]), distort_limit = ds)\n",
    "        preds = np.zeros((100))\n",
    "        for numbatch in range(totalbatch):\n",
    "            aebatch, _ = getabatch(ae_FD_GD, cleanlabel, numbatch, batchsize)\n",
    "            preds[numbatch*batchsize:(numbatch*batchsize+batchsize)] = sess.run([session_preds], {x: aebatch})[0]\n",
    "        print(\"Accuracy:\", np.mean(preds == cleanlabel))\n",
    "        print(\"Attack success rate:\", np.mean(preds == np.argmax(targets, axis=1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-06T16:00:55.887622Z",
     "start_time": "2020-05-06T15:58:29.464611Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./AE/LBFGS_AE_data.npy\n",
      "Distortion: 0.01\n",
      "Accuracy: 0.95\n",
      "Attack success rate: 0.0\n",
      "Distortion: 0.05\n",
      "Accuracy: 0.97\n",
      "Attack success rate: 0.0\n",
      "Distortion: 0.1\n",
      "Accuracy: 0.93\n",
      "Attack success rate: 0.0\n",
      "Distortion: 0.15\n",
      "Accuracy: 0.91\n",
      "Attack success rate: 0.0\n",
      "Distortion: 0.2\n",
      "Accuracy: 0.94\n",
      "Attack success rate: 0.0\n",
      "Distortion: 0.25\n",
      "Accuracy: 0.94\n",
      "Attack success rate: 0.0\n",
      "Distortion: 0.3\n",
      "Accuracy: 0.88\n",
      "Attack success rate: 0.0\n"
     ]
    }
   ],
   "source": [
    "ae_dir = \"./AE\"\n",
    "names = os.listdir(ae_dir)\n",
    "for name in names:\n",
    "    path = os.path.join(ae_dir, name)\n",
    "    if os.path.splitext(path)[-1] == '.npy' and name.find('LBFGS') != -1:\n",
    "        print(path)\n",
    "        ae_data = np.load(path)\n",
    "        for ds in [0.01, 0.05, 0.10, 0.15, 0.20, 0.25, 0.30]:\n",
    "            print(\"Distortion:\", ds)\n",
    "            ae_FD_GD = np.zeros(ae_data.shape)\n",
    "            for i in range(ae_data.shape[0]):\n",
    "                ae_FD_GD[i] = defend_GD(defend_FD_sig(ae_data[i]), distort_limit = ds)\n",
    "            preds = np.zeros((100))\n",
    "            for numbatch in range(totalbatch):\n",
    "                aebatch, _ = getabatch(ae_FD_GD, cleanlabel, numbatch, batchsize)\n",
    "                preds[numbatch*batchsize:(numbatch*batchsize+batchsize)] = sess.run([session_preds], {x: aebatch})[0]\n",
    "            print(\"Accuracy:\", np.mean(preds == cleanlabel))\n",
    "            print(\"Attack success rate:\", np.mean(preds == np.argmax(targets, axis=1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Randomization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-06T16:06:00.346593Z",
     "start_time": "2020-05-06T16:05:59.310373Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/stanislas/RDD/Random_Distortion_over_Distillation_master/defense_compare.py:205: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/stanislas/RDD/Random_Distortion_over_Distillation_master/defense_compare.py:206: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "box_ind is deprecated, use box_indices instead\n",
      "INFO:tensorflow:Scale of 0 disables regularizer.\n"
     ]
    }
   ],
   "source": [
    "x_single = tf.placeholder(tf.float32, (299, 299, 3))\n",
    "x_expanded = tf.expand_dims(x_single, axis=0)\n",
    "rand_xs = defend_randomization(x_expanded)\n",
    "rand_logits, rand_preds = inceptionv3.model(sess, rand_xs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-05T15:20:18.508083Z",
     "start_time": "2020-05-05T15:20:05.779678Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./AE/FGSM_003_AE_data.npy\n",
      "Accuracy: 0.6\n",
      "Attack success rate: 0.0\n",
      "./AE/CW_AE_data.npy\n",
      "Accuracy: 0.86\n",
      "Attack success rate: 0.0\n",
      "./AE/IFGSM_001_AE_data.npy\n",
      "Accuracy: 0.92\n",
      "Attack success rate: 0.0\n",
      "./AE/FGSM_001_AE_data.npy\n",
      "Accuracy: 0.69\n",
      "Attack success rate: 0.0\n",
      "./AE/clean100data.npy\n",
      "Accuracy: 0.96\n",
      "Attack success rate: 0.0\n",
      "./AE/IFGSM_003_AE_data.npy\n",
      "Accuracy: 0.86\n",
      "Attack success rate: 0.01\n",
      "./AE/Deepfool_AE_data.npy\n",
      "Accuracy: 0.91\n",
      "Attack success rate: 0.0\n"
     ]
    }
   ],
   "source": [
    "ae_dir = \"./AE\"\n",
    "names = os.listdir(ae_dir)\n",
    "for name in names:\n",
    "    path = os.path.join(ae_dir, name)\n",
    "    if os.path.splitext(path)[-1] == '.npy':\n",
    "        print(path)\n",
    "        ae_data = np.load(path)\n",
    "        preds = np.zeros((100))\n",
    "        for i in range(100):\n",
    "            preds[i] = sess.run([rand_preds], {x_single: ae_data[i]})[0]\n",
    "        print(\"Accuracy:\", np.mean(preds == cleanlabel))\n",
    "        print(\"Attack success rate:\", np.mean(preds == np.argmax(targets, axis=1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FD+Randomization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-05T15:30:00.098382Z",
     "start_time": "2020-05-05T15:27:24.587659Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./AE/FGSM_003_AE_data.npy\n",
      "Accuracy: 0.58\n",
      "Attack success rate: 0.0\n",
      "./AE/CW_AE_data.npy\n",
      "Accuracy: 0.86\n",
      "Attack success rate: 0.0\n",
      "./AE/IFGSM_001_AE_data.npy\n",
      "Accuracy: 0.91\n",
      "Attack success rate: 0.0\n",
      "./AE/FGSM_001_AE_data.npy\n",
      "Accuracy: 0.79\n",
      "Attack success rate: 0.0\n",
      "./AE/clean100data.npy\n",
      "Accuracy: 0.92\n",
      "Attack success rate: 0.0\n",
      "./AE/IFGSM_003_AE_data.npy\n",
      "Accuracy: 0.88\n",
      "Attack success rate: 0.0\n",
      "./AE/Deepfool_AE_data.npy\n",
      "Accuracy: 0.93\n",
      "Attack success rate: 0.0\n"
     ]
    }
   ],
   "source": [
    "ae_dir = \"./AE\"\n",
    "names = os.listdir(ae_dir)\n",
    "for name in names:\n",
    "    path = os.path.join(ae_dir, name)\n",
    "    if os.path.splitext(path)[-1] == '.npy':\n",
    "        print(path)\n",
    "        ae_data = np.load(path)\n",
    "        preds = np.zeros((100))\n",
    "        for i in range(100):\n",
    "            preds[i] = sess.run([rand_preds], {x_single: defend_FD_sig(ae_data[i])})[0]\n",
    "        print(\"Accuracy:\", np.mean(preds == cleanlabel))\n",
    "        print(\"Attack success rate:\", np.mean(preds == np.argmax(targets, axis=1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Randomization parameter test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-06T16:52:38.960544Z",
     "start_time": "2020-05-06T16:52:37.926811Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Scale of 0 disables regularizer.\n"
     ]
    }
   ],
   "source": [
    "x_single = tf.placeholder(tf.float32, (299, 299, 3))\n",
    "x_expanded = tf.expand_dims(x_single, axis=0)\n",
    "rand_xs = defend_randomization_test(x_expanded, bound=400, pad_value=1)\n",
    "rand_logits, rand_preds = inceptionv3.model(sess, rand_xs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-06T16:52:52.457463Z",
     "start_time": "2020-05-06T16:52:38.962483Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./AE/FGSM_003_AE_data.npy\n",
      "Accuracy: 0.58\n",
      "Attack success rate: 0.0\n",
      "./AE/CW_AE_data.npy\n",
      "Accuracy: 0.86\n",
      "Attack success rate: 0.0\n",
      "./AE/IFGSM_001_AE_data.npy\n",
      "Accuracy: 0.9\n",
      "Attack success rate: 0.0\n",
      "./AE/FGSM_001_AE_data.npy\n",
      "Accuracy: 0.68\n",
      "Attack success rate: 0.0\n",
      "./AE/clean100data.npy\n",
      "Accuracy: 0.95\n",
      "Attack success rate: 0.0\n",
      "./AE/LBFGS_AE_data.npy\n",
      "Accuracy: 0.93\n",
      "Attack success rate: 0.0\n",
      "./AE/IFGSM_003_AE_data.npy\n",
      "Accuracy: 0.84\n",
      "Attack success rate: 0.01\n",
      "./AE/Deepfool_AE_data.npy\n",
      "Accuracy: 0.95\n",
      "Attack success rate: 0.0\n"
     ]
    }
   ],
   "source": [
    "# Random\n",
    "ae_dir = \"./AE\"\n",
    "names = os.listdir(ae_dir)\n",
    "for name in names:\n",
    "    path = os.path.join(ae_dir, name)\n",
    "    if os.path.splitext(path)[-1] == '.npy':\n",
    "        print(path)\n",
    "        ae_data = np.load(path)\n",
    "        preds = np.zeros((100))\n",
    "        for i in range(100):\n",
    "            preds[i] = sess.run([rand_preds], {x_single: ae_data[i]})[0]\n",
    "        print(\"Accuracy:\", np.mean(preds == cleanlabel))\n",
    "        print(\"Attack success rate:\", np.mean(preds == np.argmax(targets, axis=1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-06T16:55:53.635218Z",
     "start_time": "2020-05-06T16:52:52.460708Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./AE/FGSM_003_AE_data.npy\n",
      "Accuracy: 0.6\n",
      "Attack success rate: 0.0\n",
      "./AE/CW_AE_data.npy\n",
      "Accuracy: 0.91\n",
      "Attack success rate: 0.0\n",
      "./AE/IFGSM_001_AE_data.npy\n",
      "Accuracy: 0.91\n",
      "Attack success rate: 0.0\n",
      "./AE/FGSM_001_AE_data.npy\n",
      "Accuracy: 0.75\n",
      "Attack success rate: 0.0\n",
      "./AE/clean100data.npy\n",
      "Accuracy: 0.93\n",
      "Attack success rate: 0.0\n",
      "./AE/LBFGS_AE_data.npy\n",
      "Accuracy: 0.87\n",
      "Attack success rate: 0.0\n",
      "./AE/IFGSM_003_AE_data.npy\n",
      "Accuracy: 0.83\n",
      "Attack success rate: 0.0\n",
      "./AE/Deepfool_AE_data.npy\n",
      "Accuracy: 0.93\n",
      "Attack success rate: 0.0\n"
     ]
    }
   ],
   "source": [
    "# FD+Random\n",
    "ae_dir = \"./AE\"\n",
    "names = os.listdir(ae_dir)\n",
    "for name in names:\n",
    "    path = os.path.join(ae_dir, name)\n",
    "    if os.path.splitext(path)[-1] == '.npy':\n",
    "        print(path)\n",
    "        ae_data = np.load(path)\n",
    "        preds = np.zeros((100))\n",
    "        for i in range(100):\n",
    "            preds[i] = sess.run([rand_preds], {x_single: defend_FD_sig(ae_data[i])})[0]\n",
    "        print(\"Accuracy:\", np.mean(preds == cleanlabel))\n",
    "        print(\"Attack success rate:\", np.mean(preds == np.argmax(targets, axis=1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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